General Carbon-Supporting Strategy to Boost the Oxygen Reduction Activity of Zeolitic-Imidazolate-Framework-Derived Fe/N/Carbon Catalysts in Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
The oxygen reduction reaction (ORR) activity of the Fe/N/Carbon catalysts derived from the pyrolysis of zeolitic-imidazolate-framework-8 (ZIF-8) has been still lower than that of commercial Pt-based catalysts utilized in the proton exchange membrane fuel cells (PEMFCs) due to low density of accessible active sites. In this study, an efficient carbon-supporting strategy is developed to enhance the ORR efficiency of the ZIF-derived Fe/N/Carbon catalysts by increasing the accessible active site density. The enhancement lies in (i) improving the accessibility of active sites via converting dodecahedral particles to graphene-like layered materials and (ii) enhancing the density of FeN x active sites via suppressing the formation of nanoparticles as well as providing extra spaces to host active sites. The optimized and efficient Fe/N/Carbon catalyst shows a half-wave potential ( E 1/2 ) of 0.834 V versus reversible hydrogen electrode in acidic media and produces a peak power density of 0.66 W cm –2 in an air-fed PEMFC at 2 bar backpressure, outperforming most previously reported Pt-free ORR catalysts. Finally, the general applicability of the carbon-supporting strategy is confirmed using five different commercial carbon blacks. This work provides an effective route to derive Fe/N/Carbon catalysts exhibiting a higher power density in PEMFCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".